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Zero-shot Point Cloud Completion Using 2D Diffusion Model Priors


Core Concepts
A zero-shot framework for completing partial point clouds by leveraging 2D priors from pre-trained diffusion models through 3D Gaussian Splatting, eliminating the need for any additional manual information.
Abstract
The paper proposes a zero-shot point cloud completion framework that utilizes 2D priors from pre-trained diffusion models to complete partial point clouds without requiring any extra manual information such as text descriptions. The framework consists of two main components: Point Cloud Colorization: Estimates a reference camera pose to capture the most complete observation of the partial point cloud. Generates a colorized reference image of the partial point cloud using 3D Gaussian Splatting and depth-conditioned ControlNet. Zero-shot Fractal Completion (ZFC): Optimizes 3D Gaussians to complete the missing regions of the point cloud, guided by view-dependent guidance from the Zero 1-to-3 diffusion model conditioned on the reference image. Incorporates a Preservation Constraint to maintain the geometric integrity of the partial point cloud. Extracts the completed point cloud from the optimized 3D Gaussians and resamples it to a uniform distribution using a Grid Pulling module. The authors demonstrate that their approach outperforms existing network-based completion methods on both synthetic and real-world scanned point clouds, without any requirement for specific training data or manual prompts.
Stats
"3D point clouds have always been an important representation for the physical 3D world with extensive use in various applications such as SLAM or 3D detection." "Effective and robust completion for partial point clouds can greatly reduce the cost for data collection, and are also useful for subsequent perception of the 3D world." "Existing network-based completion methods are often constrained to object categories similar to those seen during training, limiting their effectiveness on unseen data."
Quotes
"Leveraging the impressive capabilities of 2D diffusion models, Kasten et al. [13] propose a test-time point cloud completion methods utilizing text-to-3D generative models [24, 30]. However, a notable limitation of the method proposed by Kasten et al. [13] is its dependency on manually created text prompts for each point cloud to guide the completion." "Motivated by the amodal perception [1, 16], we aim to complete a point cloud with its observation from a reference viewpoint in this work."

Key Insights Distilled From

by Tianxin Huan... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06814.pdf
Zero-shot Point Cloud Completion Via 2D Priors

Deeper Inquiries

How could the proposed framework be extended to handle dynamic point clouds or point clouds with varying densities

To extend the proposed framework to handle dynamic point clouds or point clouds with varying densities, several modifications and additions can be made. One approach could involve incorporating temporal information into the completion process to account for changes in the point cloud over time. This could be achieved by introducing a recurrent neural network (RNN) or a similar architecture that can process sequential data and adapt to dynamic point cloud inputs. Additionally, the framework could be enhanced with adaptive sampling techniques that adjust the density of points based on the local geometry of the point cloud. By dynamically adjusting the sampling density, the completion process can better capture intricate details and variations in the point cloud.

What are the potential limitations of the Gaussian Splatting approach, and how could it be further improved to handle more complex point cloud geometries

The Gaussian Splatting approach, while effective in rendering 3D point clouds into 2D images for colorization and guidance, may have limitations when handling more complex point cloud geometries. One potential limitation is the assumption of uniform spherical shapes for the 3D Gaussians, which may not accurately represent the diverse shapes and structures present in real-world point clouds. To address this limitation, the Gaussian Splatting approach could be further improved by incorporating adaptive Gaussian shapes that can better conform to the local geometry of the point cloud. This adaptive Gaussian modeling could involve using learned shape priors or hierarchical Gaussian representations to capture the varying structures within the point cloud more effectively.

Given the reliance on pre-trained 2D diffusion models, how could the framework be adapted to leverage emerging 3D-specific generative models as they become more widely available

As the reliance on pre-trained 2D diffusion models provides a strong foundation for leveraging 2D priors in the completion framework, adapting the framework to incorporate emerging 3D-specific generative models can further enhance its capabilities. One approach could involve integrating 3D generative models, such as voxel-based or mesh-based models, into the completion pipeline. By combining the strengths of both 2D and 3D generative models, the framework can benefit from the richer spatial information and structural understanding offered by 3D models. This integration could enable more accurate and detailed completions of 3D point clouds, especially in scenarios where complex 3D geometries and textures need to be inferred. Additionally, fine-tuning the framework with 3D-specific generative models can help adapt it to a wider range of 3D reconstruction tasks and improve its performance on diverse point cloud datasets.
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